Approximations of Path-distribution Dependent Stochastic Differential Equations Driven by α-stable Noise

Key Points

• Path-distribution dependent stochastic differential equations are effectively approximated, enhancing model accuracy.
• Key evidence indicates significant reductions in approximation error with α-stable noise integration.
• Observational analysis of driven processes validates the robustness of the proposed approximation methods.
• Enhancements may support broader applications in financial modeling, indicating practical significance.